《海外直订Machine Learning for Model Order Reduction 模型降阶的机器学习》,作者:海外直订Machine Learning for Model Order Reduction 模型降阶的机器学习Mohamed 著,出版社:Springer,ISBN:9783030093075。
A machine learning closure approach is proposed for reduced order modeling of thermal fluids. • A single hidden layer feed forward neural network architecture is developed for Boussinesq equations. • An extreme learning machine strategy is implemented for fast training. • The robustness of the...
In this paper, we propose a data-driven model order reduction method to solve parameterized time-dependent partial differential equations. We describe the system with the state variable equations, and represent a class of candidate models with the artificial neural network. The discreteL2\documentclass...
Physics-informed machine learning of reduced-order model without requirement of extra high-fidelity snapshots. • A PINN trained by minimizing the residual loss of the reduced-order equation. • A PRNN with improved accuracy obtained by adding the regression loss on the available high-fidelity ...
We show that the use of ANN models inside the weak-constraint 4D-Var framework has the potential to extend the applicability of the weak-constraint methodology for model error correction to the whole atmospheric column. Finally, we discuss the potential and limitations of the machine learning/deep...
The first of these challenges is particularly relevant when integrating machine-learning models as layers within the funnel since it is often impossible to know the true accuracy of a data-driven model ahead of time, for an arbitrary data-point, since generalised performance is often intrinsically ...
A method or a system for using machine learning to dynamically boost order delivery time. The system receives an order associated with a delivery time and a compensation value. The system applies a machine-learning model to an order to predict an amount of lateness time that an order will be...
Therefore, we aimed to reconstruct CHL data without missing values based on microwave and reanalysis data, which was immune to clouds, using the machine learning-based model on a regional scale, near Cape Hallett, Ross Sea, Antarctica. As predictors for the model, we considered various factors ...
Explore advancements in state of the art machine learning research in speech and natural language, privacy, computer vision, health, and more.
PINNACLEis a context-specific geometric deep learning model for generating protein representations. Leveraging single-cell transcriptomics combined with networks of protein–protein interactions, cell type-to-cell type interactions and a tissue hierarchy, PINNACLE generates high-resolution protein representations...